Template-Type: ReDIF-Paper 1.0
Author-Name: van Wezel, M.C.
Author-Name-Last: van Wezel
Author-Name-First: Michiel
Author-Name: Kagie, M.
Author-Name-Last: Kagie
Author-Name-First: Martijn
Author-Person: pka404
Author-Name: Potharst, R.
Author-Name-Last: Potharst
Author-Name-First: Rob
Title: Boosting the accuracy of hedonic pricing models
Abstract: Hedonic pricing models attempt to model a relationship between object attributes and the object's price. Traditional hedonic pricing models are often parametric models that suffer from misspecification. In this paper we create these models by means of boosted CART models. The method is explained in detail and applied to various datasets. Empirically, we find substantial reduction of errors on out-of-sample data for two out of three datasets compared with a stepwise linear regression model. We interpret the boosted models by partial dependence plots and relative importance plots. This reveals some interesting nonlinearities and differences in attribute importance across the model types.
Creation-Date: 2005-12-02
File-URL: https://repub.eur.nl/pub/7145/ei2005-50.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2005-50
Keywords: conjoint analysis, data mining, ensemble learning, gradient boosting, hedonic pricing, marketing, pricing
Handle: RePEc:ems:eureir:7145